Imports
library(notly)
Attaching package: ‘notly’
The following object is masked from ‘package:plotly’:
ggplotly
View a fitness landscape
p <- plotFitnessLandscape()
save_dir <- file.path(output_dir, "FitnessFunctions")
if (!dir.exists(save_dir)) dir.create(save_dir)
save_dir <- file.path(save_dir, format(Sys.time(), "%F_%H_%M_%S"))
if (!dir.exists(save_dir)) dir.create(save_dir)
fname <- file.path(save_dir, "fitness_function.html")
htmlwidgets::saveWidget(as_widget(p), fname)
Plot different trait architectures according to different
algorithms
save_dir <- file.path(output_dir, "TraitArchitecture")
if (!dir.exists(save_dir)) dir.create(save_dir)
save_dir <- file.path(save_dir, format(Sys.time(), "%F_%H_%M_%S"))
if (!dir.exists(save_dir)) dir.create(save_dir)
write.table(getParams(), file.path(save_dir, "params.txt"), col.names=FALSE, quote=FALSE, sep=":\t")
# Additive effects
methodType <- "Additive"
g <- plotTraitArchitecture(founderPop, methodType)
ggplot2::ggsave(filename = paste0("traitarchitecture_", methodType, ".pdf"),
path=save_dir,
device = "pdf")
# Effect of each allele on fitness
methodType <- "Fitness"
g <- plotTraitArchitecture(founderPop, methodType)
ggplot2::ggsave(filename = paste0("traitarchitecture_", methodType, ".pdf"),
path=save_dir,
device = "pdf")
p <- plot3dPopulationFitness(founderPop, calculateFitnessTwoTrait)
fname <- file.path(save_dir, "3dpopulationfitness.html")
htmlwidgets::saveWidget(as_widget(p), fname)
Simulate several adaptive walks with different population sizes
# Different starting population sizes
subPopSizes <- c(50,500)
selProps <- c(0.4,0.4)
n.h2 <- 0.3
n.var <- 0.1
n.qtlPerChr <- 2
n.gens <- 50
# Don't use a SNP chip for these simulations
addSnpChip <- FALSE
fig <- plot_ly() %>%
layout(legend=list(title=list(text='Population Size')),
scene = list(xaxis = list(title = "Trait A"),
yaxis = list(title = "Trait B"),
zaxis = list(title = "Fitness"),
aspectmode='cube')) #hide_colorbar()
#source("Scripts/CreateFounderPop.R")
pop <- founderPop
for (gen in 1:n.burnInGens) {
pop <- selectCross(pop, trait=twoTraitFitFunc, nInd=nInd(pop)*n.burnInSelProp, nCrosses=nInd(pop))
}
# Iterate through each population size
for (p in 1:length(subPopSizes)) {
n.subPopSize <- subPopSizes[p]
n.selProp <- selProps[p]
print(n.subPopSize)
fit_df <- data.frame(gen=1:n.gens,
fitness=numeric(n.gens),
traitValA=numeric(n.gens),
traitValB=numeric(n.gens))
subPop <- selectInd(pop, nInd=n.subPopSize, use="rand")
# Iterate through the generations, update the result dataframe, and advance
# progeny based on the two trait fitness funcion
for(gen in 1:n.gens) {
meanFitness <- mean(twoTraitFitFunc(pheno(subPop)))
fit_df$fitness[gen] <- calculateFitnessTwoTraitModified(meanP(subPop)[1], meanP(subPop)[2])
fit_df$traitValA[gen] <- meanP(subPop)[1]
fit_df$traitValB[gen] <- meanP(subPop)[2]
selRat <- selectionRatio(meanFitness)
subPop <- selectCross(subPop, trait=twoTraitFitFunc, nInd=n.subPopSize*selRat, nCrosses=n.subPopSize)
}
# Add a trace to represent this population's adaptive walk
fig <- fig %>% add_trace(
fig,
fit_df,
name = n.subPopSize,
x = fit_df$traitValA,
y = fit_df$traitValB,
z = fit_df$fitness,
type = 'scatter3d',
mode = 'lines',
opacity = 1,
line = list(autocolorscale=FALSE, colors="PuBuGn", color=p, which=2, width = 10)
)
}
[1] 50
[1] 500
# Create a matrix of fitness values, with a small increment along the x and y axes.
fitness_x = seq(n.initTraitVal*-1,n.initTraitVal*1, by=(n.initTraitVal/100))
fitness_y = seq(n.initTraitVal*-1,n.initTraitVal*1, by=(n.initTraitVal/100))
fitness_z = outer(fitness_x,fitness_y,calculateFitnessTwoTrait)
fig <- fig %>%
add_trace(
fig,
x=fitness_x,
y=fitness_y,
z=fitness_z,
type='surface',
colorbar=list(title = "Fitness"),
colors = viridis(n=10),
opacity=1.0)
fig
save_dir <- file.path(output_dir, "DifferentPopulationSizes")
if (!dir.exists(save_dir)) dir.create(save_dir)
save_dir <- file.path(save_dir, format(Sys.time(), "%F_%H_%M_%S"))
if (!dir.exists(save_dir)) dir.create(save_dir)
write.table(getParams(), file.path(save_dir, "params.txt"), col.names=FALSE, quote=FALSE, sep=":\t")
fname <- file.path(save_dir, paste0("populationSizes_", subPopSizes[1], ",", subPopSizes[2], ",", subPopSizes[3], ".html"))
htmlwidgets::saveWidget(as_widget(fig), fname)
Overlay an adaptive walk over a fitness landscape, and save 3D and 2D
versions
fig <- plot_ly()
fit_df <- data.frame(gen=1:n.gens,
fitness=numeric(n.gens),
traitValA=numeric(n.gens),
traitValB=numeric(n.gens))
pop <- founderPop
for(gen in 1:n.gens) {
fit_df$fitness[gen] <- mean(twoTraitFitFunc(gv(pop)))
fit_df$traitValA[gen] <- meanG(pop)[1]
fit_df$traitValB[gen] <- meanG(pop)[2]
meanFitness <- mean(twoTraitFitFunc(pheno(pop)))
selRat <- selectionRatio(meanFitness)
pop <- selectCross(pop, trait=twoTraitFitFunc, nInd=n.popSize*selRat, nCrosses=n.popSize)
}
save_dir <- file.path(output_dir, "OverlayAdaptiveWalk")
if (!dir.exists(save_dir)) dir.create(save_dir)
save_dir <- file.path(save_dir, format(Sys.time(), "%F_%H_%M_%S"))
if (!dir.exists(save_dir)) dir.create(save_dir)
plotType <- "CONTOUR"
pc <- overlayWalkOnLandscape(fit_df, type=plotType, calculateFitnessTwoTrait)
fname <- file.path(save_dir, paste0("adaptivewalk_", plotType, ".html"))
htmlwidgets::saveWidget(as_widget(pc), fname)
plotType <- "SURFACE"
ps <- overlayWalkOnLandscape(fit_df, type=plotType, calculateFitnessTwoTrait)
fname <- file.path(save_dir, paste0("adaptivewalk_", plotType, ".html"))
htmlwidgets::saveWidget(as_widget(ps), fname)
write.table(getParams(), file.path(save_dir, "params.txt"), col.names=FALSE, quote=FALSE, sep=":\t")
Plot the change in allele frequency over time
n.qtlPerChr <- 2
source("Scripts/CreateFounderPop.R")
pop <- founderPop
# Results dataframe
freq.df <- data.frame(gen=1:n.gens)
# Get the effect sizes of each qtl
qtlEff.df <- getQtlEffectSizes(pop)
# Get the names of all the QTLs
qtl <- colnames(getUniqueQtl(pop))
# Create a dataframe of all zeros where the columns are the QTL ids, and the # rows is the # of generations
qtl.df <- data.frame(matrix(0, ncol=length(qtl), nrow=n.gens))
colnames(qtl.df) <- qtl
# Combine the dataframes
freq.df <- cbind(freq.df, qtl.df)
# Iterate through each generation
for(gen in 1:n.gens) {
meanFitness <- mean(twoTraitFitFunc(pheno(pop)))
# Get the qtl genotype data
qtlGeno <- getUniqueQtl(pop)
# Get the frequency of the '2' allele at each locus
for (l in 1:length(qtl)) {
# id is the name of the qtl (chr_site)
id <- qtl[l]
# A list of genotype data for each individual in the population at that locus
locus <- qtlGeno[,id]
# Calculate the allele frequency as the frequency of homozygous individuals (for 'allele') +
# 1/2 * frequency of heterozygous individuals (assumes the locus is biallelic)
freq.df[gen,id] <- (sum(locus==n.allele)/n.popSize) + ((sum(locus==1)/n.popSize)/2)
}
# Determine selection ration based on fitness
selRat <- selectionRatio(meanFitness)
#Advance individuals
pop <- selectCross(pop, trait=twoTraitFitFunc, nInd=n.popSize*selRat, nCrosses=n.popSize)
}
# Make the dataframe tidy
freq.df<- melt(freq.df, id="gen", variable.name="QTL ID", value.name="Allele Frequency")
# Add the qtl effect size data to the dataframe
freq.df <- merge(freq.df, qtlEff.df, by.x="QTL ID", by.y="row.names", all.x=TRUE)
# Create a line plot for the change in frequency of alleles over time
# Each line's opacity is a function of its effect size (higher=darker)
g <- ggplot(freq.df, aes(x=gen, y=freq, color=id, alpha=eff_size)) +
geom_line(size=0.7)
save_dir <- file.path(output_dir, "AlleleFrequencies")
if (!dir.exists(save_dir)) dir.create(save_dir)
save_dir <- file.path(save_dir, format(Sys.time(), "%F_%H_%M_%S"))
if (!dir.exists(save_dir)) dir.create(save_dir)
write.table(getParams(), file.path(save_dir, "params.txt"), col.names=FALSE, quote=FALSE, sep=":\t")
ggplot2::ggsave(filename = paste0("allelefrequencies.pdf"),
path=save_dir,
device = "pdf",
width=10,
height=7)
Simulate an adaptive walk, and plot the following curve: #segregating
QTL/#segregating loci
Original question: out of segregating alleles left in the population,
how many of them would increase fitness? Is this even a valid
question?
TODO: - How does this change with size of allele? - How does size of
‘fitness delta’ change over generations? - Simualate mutations for a
single individual to figure out P(allelic substitution is favorable),
should reflect FKO - P(mutation gets fixed) - show that this matches
Kimura - Compare adaptive walks for DH/inbred population where there are
new mutations VS population w/standing genetic variation
n.gens <- 200
# Reset variables
source("Scripts/CreateFounderPop.R")
source("Scripts/GlobalParameters.R")
pop <- founderPop
df <- data.frame(gen=c(),
fitness=c(),
traitVal1=c(),
traitVal2=c(),
percFitInc=c(),
segAlleles=c(),
segQtl=c())
# Iterate through the generations
for (gen in 1:n.gens) {
print(gen)
# Counter variables
numHetLoci = 0
numHetQtl = 0
numInc = 0
# Get all of the loci from the population
segSiteGeno <- pullSegSiteGeno(pop)
# Get all of the QTL from the population (a sampling of the segSiteGeno)
qtlGeno <- getUniqueQtl(pop)
qtlLoci <- colnames(qtlGeno)
loci <- colnames(segSiteGeno)
nLoci <- ncol(segSiteGeno)
# Iterate through all of the loci
# TODO: just calculate numHetQtl / numHetLoci
for (l in 1:ncol(segSiteGeno)) {
id <- loci[l]
locus = segSiteGeno[,l]
# Check if the locus is segregating
if (hetLocus(locus)) {
# Increment the counter
numHetLoci <- numHetLoci + 1
# Determine the effect size - this is where the bug is
#e <- getEffectSize(locus, id, pop, "Fitness")
#if (e > 0) {
# numInc <- numInc +1
#}
# If the locus is a QTL, then it has an effect size
if (id %in% qtlLoci) {
numHetQtl <- numHetQtl + 1
}
}
}
# Calculate the ratio of segregating QTL to segregating alleles
if (numHetLoci == 0) {
perc <- 0
} else {
perc <- (numHetQtl / numHetLoci)
}
df <- rbind(df, data.frame(gen=gen,
fitness=mean(twoTraitFitFunc(gv(pop))),
traitVal1=meanG(pop)[1],
traitVal2=meanG(pop)[2],
percFitInc=perc,
segAlleles=numHetLoci,
segQtl=numHetQtl))
# If the population is within n.margin, terminate the simulation
if (mean(twoTraitFitFunc(gv(pop))) >= n.margin) {
break
}
meanFitness <- mean(twoTraitFitFunc(pheno(pop)))
selRat <- selectionRatio(meanFitness)
# Advance the top progeny according to the fitness function
pop <- selectCross(pop=pop, trait=twoTraitFitFunc, nInd=nInd(pop)*selRat, nCrosses=nInd(pop))
}
save_dir <- file.path(output_dir, "BeneficialAlleles")
if (!dir.exists(save_dir)) dir.create(save_dir)
save_dir <- file.path(save_dir, format(Sys.time(), "%F_%H_%M_%S"))
if (!dir.exists(save_dir)) dir.create(save_dir)
write.table(getParams(), file.path(save_dir, "params.txt"), col.names=FALSE, quote=FALSE, sep=":\t")
fname <- file.path(save_dir, "beneficialalleles.pdf")
pdf(fname)
# Plot the ratio of segregating QTL to segregating alleles, along with fitness
par(mar = c(5, 5, 3, 5) + 0.3)
plot(df$gen, df$fitness, type="l", lwd = "3", col=2, xlab = "Generation", ylab = "Fitness")
par(new = TRUE)
plot(df$gen, df$percFitInc, type="l", lwd = "3", col = 3, axes = FALSE, xlab = "", ylab = "")
axis(side = 4, at = pretty(range(df$percFitInc)))
mtext("% of Segregating alleles that are QTL", side = 4, line = 3)
par(xpd=TRUE)
legend("right",
c("Fitness", "% Alleles"),
lty = 1,
lwd = 3,
col = 2:3)
dev.off()
This block runs n.nPops * n.sims Monte Carlo simulations of different
populations to determine the change in the average effect size of
alleles that are fixed along the adaptive walk, saving the order in
which the alleles are fixed n.nPops populations are created, and each
one undergoes n.sims unique adaptive walks

This block will simulate one base population, from which two
sub-populations are selected, and undergo purifying selection
independently.
source("Scripts/GlobalParameters.R")
source("Scripts/CreateFounderPop.R")
fit_df <- data.frame(gen=1:n.burnInGens,
fitness=numeric(n.burnInGens),
traitValA=numeric(n.burnInGens),
traitValB=numeric(n.burnInGens))
pop <- founderPop
# Burn-in generations
for (gen in 1:n.burnInGens) {
fit_df$fitness[gen] <- mean(twoTraitFitFunc(pheno(pop)))
fit_df$traitValA[gen] <- meanP(pop)[1]
fit_df$traitValB[gen] <- meanP(pop)[2]
pop <- selectCross(pop, trait=twoTraitFitFunc, nInd=nInd(pop)*n.burnInSelProp, nCrosses=nInd(pop))
}
# Create a random vector of size n.pops, with a random order of sub-population ids
randVec <- sample(rep(c(1:n.nPops), times=n.popSize/n.nPops))
# Select all of the "1" indexed individuals
popA <- selectInd(pop, trait=selectSubPop, selectTop=TRUE, nInd=n.subPopSize, idx=1, randVec=randVec)
# Select all of the "2" indexed individuals
popB <- selectInd(pop, trait=selectSubPop, selectTop=TRUE, nInd=n.subPopSize, idx=2, randVec=randVec)
# Create dataframes for each subpopulation, initializing with current values
popA_df <- data.frame(gen=c(1),
fitness=c(mean(twoTraitFitFunc(pheno(popA)))),
traitValA=c(meanP(popA)[1]),
traitValB=c(meanP(popA)[2]))
popB_df <- data.frame(gen=c(1),
fitness=c(mean(twoTraitFitFunc(pheno(popB)))),
traitValA=c(meanP(popB)[1]),
traitValB=c(meanP(popB)[2]))
# Iterate through the generations
for (gen in 1:n.gens) {
# If popA is within the margin of the fitness optimum, don't progress it any further
if (mean(twoTraitFitFunc(pheno(popA))) < n.margin) {
# Advance the population
meanFitness <- mean(twoTraitFitFunc(pheno(popA)))
# Get a selection ratio based on fitness
selRat <- selectionRatio(meanFitness)
popA <- selectCross(popA, trait=twoTraitFitFunc, nInd=nInd(popA)*selRat, nCrosses=nInd(popA))
# Update the dataframe with new values
popA_df <- rbind(popA_df, data.frame(gen=gen,
fitness=meanFitness,
traitValA=meanP(popA)[1],
traitValB=meanP(popA)[2]))
}
# If popB is within the margin of the fitness optimum, don't progress it any further
if (mean(twoTraitFitFunc(pheno(popB))) < n.margin) {
# If popA is within the margin of the fitness optimum, don't progress it any further
meanFitness <- mean(twoTraitFitFunc(pheno(popB)))
# Get a selection ratio based on fitnes
selRat <- selectionRatio(meanFitness)
popB <- selectCross(popB, trait=twoTraitFitFunc, nInd=nInd(popB)*selRat, nCrosses=nInd(popB))
# Update the dataframe with new values
popB_df <- rbind(popB_df, data.frame(gen=gen,
fitness=meanFitness,
traitValA=meanP(popB)[1],
traitValB=meanP(popB)[2]))
}
}
# Update rownames
rownames(popA_df) <- 1:nrow(popA_df)
rownames(popB_df) <- 1:nrow(popB_df)
# Plot the adaptive walks
fig <- plot_ly()
fig <- add_trace(
fig,
fit_df,
name = "Burn In",
x = fit_df$traitValA,
y = fit_df$traitValB,
z = fit_df$fitness,
type = 'scatter3d',
mode = 'lines',
opacity = 1,
color = 'yellow',
line = list(width = 5)
)
fig <- add_trace(
fig,
popA_df,
name = "Pop A",
x = popA_df$traitValA,
y = popA_df$traitValB,
z = popA_df$fitness,
type = 'scatter3d',
mode = 'lines',
opacity = 1,
color = 'red',
line = list(width = 5)
)
fig <- add_trace(
fig,
popB_df,
name = "Pop B",
x = popB_df$traitValA,
y = popB_df$traitValB,
z = popB_df$fitness,
type = 'scatter3d',
mode = 'lines',
opacity = 1,
color = 'blue',
line = list(width = 5)
)
p <- fig %>%
layout(legend=list(title=list(text='Population')),
showlegend=FALSE,
scene = list(xaxis = list(title = "Trait A"),
yaxis = list(title = "Trait B"),
zaxis = list(title = "Fitness"),
aspectmode='cube')) %>% hide_colorbar()
save_dir <- file.path(output_dir, "DivergingPopulations")
if (!dir.exists(save_dir)) dir.create(save_dir)
save_dir <- file.path(save_dir, format(Sys.time(), "%F_%H_%M_%S"))
if (!dir.exists(save_dir)) dir.create(save_dir)
fname <- file.path(save_dir, "adaptivewalks.html")
htmlwidgets::saveWidget(as_widget(p), fname)
fname <- file.path(save_dir, "2PopulationFitness.html")
p <- plot3dPopulationFitnessTwoPops(popA, popB)
htmlwidgets::saveWidget(as_widget(p), fname)
write.table(getParams(), file.path(save_dir, "params.txt"), col.names=FALSE, quote=FALSE, sep=":\t")
fname <- file.path(save_dir, "traitarchitecture.pdf")
pdf(fname)
p1 <- plotTraitArchitecture(popA, "Fitness", "popA")
p2 <- plotTraitArchitecture(popB, "Fitness", "popB")
p3 <- plotTraitArchitecture(popA, "Additive", "popA")
p4 <- plotTraitArchitecture(popB, "Additive", "popB")
(p1|p2)/(p3|p4)
dev.off()
# Create a density plot of trait 1
trait1.df <- as.data.frame(cbind(pheno(popA)[,1], pheno(popB)[,1]))
colnames(trait1.df) <- c("popA", "popB")
trait1.df <- trait1.df %>%
pivot_longer(c("popA", "popB"), names_to="pop", values_to="pheno")
t1 <- ggplot(trait1.df, aes(pheno, fill=pop, color=pop)) +
geom_density(alpha=0.1) +
labs(title="Trait 1")
# Create a density plot of trait 2
trait2.df <- as.data.frame(cbind(pheno(popA)[,2], pheno(popB)[,2]))
colnames(trait2.df) <- c("popA", "popB")
trait2.df <- trait2.df %>%
pivot_longer(c("popA", "popB"), names_to="pop", values_to="pheno")
t2 <- ggplot(trait2.df, aes(pheno, fill=pop, color=pop)) +
geom_density(alpha=0.1) +
labs(title="Trait 2")
(t1|t2)
ggplot2::ggsave(filename = "trait_distributions.pdf",
path=save_dir,
device = "pdf")
source("Scripts/GlobalParameters.R")
n.qtlPerChr <- 2
n.h2 <- 0.3
n.qtlPerChr <- 2
n.gens <- 100
#source("Scripts/CreateFounderPop.R")
n.selProp <- 0.5
n.subPopSize <- 50
fit_df <- data.frame(gen=1:n.burnInGens,
fitness=numeric(n.burnInGens),
traitValA=numeric(n.burnInGens),
traitValB=numeric(n.burnInGens))
pop <- founderPop
# Burn-in generations
for (gen in 1:n.burnInGens) {
fit_df$fitness[gen] <- mean(twoTraitFitFunc(pheno(pop)))
fit_df$traitValA[gen] <- meanP(pop)[1]
fit_df$traitValB[gen] <- meanP(pop)[2]
pop <- selectCross(pop, trait=twoTraitFitFunc, nInd=nInd(pop)*n.burnInSelProp, nCrosses=nInd(pop))
}
# Create a random vector of size n.pops, with a random order of sub-population ids
randVec <- sample(rep(c(1:n.nPops), times=n.popSize/n.nPops))
# Select all of the "1" indexed individuals
popA <- selectInd(pop, trait=selectSubPop, selectTop=TRUE, nInd=n.subPopSize, idx=1, randVec=randVec)
# Select all of the "2" indexed individuals
popB <- selectInd(pop, trait=selectSubPop, selectTop=TRUE, nInd=n.subPopSize, idx=2, randVec=randVec)
# Create dataframes for each subpopulation, initializing with current values
popA_df <- data.frame(gen=c(1),
fitness=c(mean(twoTraitFitFunc(pheno(popA)))),
traitValA=c(meanP(popA)[1]),
traitValB=c(meanP(popA)[2]))
popB_df <- data.frame(gen=c(1),
fitness=c(mean(twoTraitFitFunc(pheno(popB)))),
traitValA=c(meanP(popB)[1]),
traitValB=c(meanP(popB)[2]))
# Iterate through the generations
for (gen in 1:n.gens) {
# If popA is within the margin of the fitness optimum, don't progress it any further
if (mean(twoTraitFitFunc(pheno(popA))) < n.margin) {
# Advance the population
meanFitness <- mean(twoTraitFitFunc(pheno(popA)))
# Get a selection ratio based on fitness
selRat <- selectionRatio(meanFitness)
popA <- selectCross(popA, trait=twoTraitFitFunc, nInd=nInd(popA)*selRat, nCrosses=nInd(popA))
# Update the dataframe with new values
popA_df <- rbind(popA_df, data.frame(gen=gen,
fitness=meanFitness,
traitValA=meanP(popA)[1],
traitValB=meanP(popA)[2]))
}
# If popB is within the margin of the fitness optimum, don't progress it any further
if (mean(twoTraitFitFunc(pheno(popB))) < n.margin) {
# If popA is within the margin of the fitness optimum, don't progress it any further
meanFitness <- mean(twoTraitFitFunc(pheno(popB)))
# Get a selection ratio based on fitnes
selRat <- selectionRatio(meanFitness)
popB <- selectCross(popB, trait=twoTraitFitFunc, nInd=nInd(popB)*selRat, nCrosses=nInd(popB))
# Update the dataframe with new values
popB_df <- rbind(popB_df, data.frame(gen=gen,
fitness=meanFitness,
traitValA=meanP(popB)[1],
traitValB=meanP(popB)[2]))
}
}
# Update rownames
rownames(popA_df) <- 1:nrow(popA_df)
rownames(popB_df) <- 1:nrow(popB_df)
# Plot the adaptive walks
# Create a matrix of fitness values, with a small increment along the x and y axes.
fitness_x = seq(n.initTraitVal*-1,n.initTraitVal*1, by=(n.initTraitVal/100))
fitness_y = seq(n.initTraitVal*-1,n.initTraitVal*1, by=(n.initTraitVal/100))
fitness_z = outer(fitness_x,fitness_y,calculateFitnessTwoTrait)
fig <- plot_ly() %>%
layout(xaxis = list(title = "Trait A", constrain = "domain"),
yaxis = list(title = "Trait B", scaleanchor="x")) %>%
add_trace(
fig,
x=fitness_x,
y=fitness_y,
z=fitness_z,
type='contour',
colors = viridis(n=10),
colorbar=list(title = "Fitness"),
line = list(color = 'black', width = 1),
opacity=1) %>%
add_trace(
fig,
fit_df,
name = "Burn-in",
x = fit_df$traitValA,
y = fit_df$traitValB,
type='scatter',
mode = 'lines',
line = list(color = 'orange', width = 4, dash = 'solid'),
opacity = 1) %>%
add_trace(
fig,
popA_df,
name = "Subpop 1",
x = popA_df$traitValA,
y = popA_df$traitValB,
type='scatter',
mode = 'lines',
line = list(color = 'red', width = 4, dash = 'solid'),
opacity = 1) %>%
add_trace(
fig,
popB_df,
name = "Subpop 2",
x = popB_df$traitValA,
y = popB_df$traitValB,
type='scatter',
mode = 'lines',
line = list(color = 'blue', width = 4, dash = 'solid'),
opacity = 1)
save_dir <- file.path(output_dir, "DivergingPopulations")
if (!dir.exists(save_dir)) dir.create(save_dir)
save_dir <- file.path(save_dir, format(Sys.time(), "%F_%H_%M_%S"))
if (!dir.exists(save_dir)) dir.create(save_dir)
fname <- file.path(save_dir, "adaptivewalks.html")
htmlwidgets::saveWidget(as_widget(fig), fname)
fname <- file.path(save_dir, "2PopulationFitness.html")
p <- plot3dPopulationFitnessTwoPops(popA, popB)
htmlwidgets::saveWidget(as_widget(p), fname)
write.table(getParams(), file.path(save_dir, "params.txt"), col.names=FALSE, quote=FALSE, sep=":\t")
fname <- file.path(save_dir, "traitarchitecture.pdf")
pdf(fname)
p1 <- plotTraitArchitecture(popA, "Fitness", "popA")
p2 <- plotTraitArchitecture(popB, "Fitness", "popB")
p3 <- plotTraitArchitecture(popA, "Additive", "popA")
p4 <- plotTraitArchitecture(popB, "Additive", "popB")
(p1|p2)/(p3|p4)
dev.off()
null device
1
# Create a density plot of trait 1
trait1.df <- as.data.frame(cbind(pheno(popA)[,1], pheno(popB)[,1]))
colnames(trait1.df) <- c("popA", "popB")
trait1.df <- trait1.df %>%
pivot_longer(c("popA", "popB"), names_to="pop", values_to="pheno")
t1 <- ggplot(trait1.df, aes(pheno, fill=pop, color=pop)) +
geom_density(alpha=0.1) +
labs(title="Trait 1")
# Create a density plot of trait 2
trait2.df <- as.data.frame(cbind(pheno(popA)[,2], pheno(popB)[,2]))
colnames(trait2.df) <- c("popA", "popB")
trait2.df <- trait2.df %>%
pivot_longer(c("popA", "popB"), names_to="pop", values_to="pheno")
t2 <- ggplot(trait2.df, aes(pheno, fill=pop, color=pop)) +
geom_density(alpha=0.1) +
labs(title="Trait 2")
(t1|t2)
ggplot2::ggsave(filename = "trait_distributions.pdf",
path=save_dir,
device = "pdf")
Saving 7.29 x 4.51 in image

---
title: "Adaptive Walks"
output: html_notebook
author: Ted Monyak
description: This notebook contains scripts for understanding the dynamics of adaptive walks.
---
```{r setup, include=FALSE, echo=FALSE}
require("knitr")
opts_knit$set(root.dir = "~/Documents/CSU/R/BreedingSims")
```

Imports
```{r}
library(AlphaSimR)
library(devtools)
library(dplyr)
library(ggplot2)
library(factoextra)
library(patchwork)
library(plotly)
library(purrr)
library(reshape2)
library(snow)
library(tibble)
library(tidyr)
library(viridis)
devtools::install_github("gdmcdonald/notly")
library(notly)
rm(list = ls())

set.seed(123)

source("Functions/Fitness.R")
source("Functions/TraitArchitecture.R")
source("Scripts/GlobalParameters.R")
source("Scripts/CreateFounderPop.R")

output_dir <- file.path(getwd(), "Output")
if (!dir.exists(output_dir)) dir.create(output_dir)

theme <- theme(
       axis.title.x = element_text(family="Helvetica", size=10),
       axis.title.y = element_text(family="Helvetica", size=10),
       plot.title = element_text(family="Helvetica", size=12, hjust = 0.5),
       legend.text = element_text(family="Helvetica", size=6),
       legend.title = element_text(family="Helvetica", size=6),
       legend.key = element_rect(linewidth=0.05),
       plot.caption = element_text(family="Helvetica", size=10, hjust = 0.5),
       panel.grid.major = element_blank(),
       panel.grid.minor = element_blank(),
       panel.background = element_rect(fill = "white", color = "black"),
       aspect.ratio = 1)
```

View a fitness landscape
```{r}
p <- plotFitnessLandscape()

save_dir <- file.path(output_dir, "FitnessFunctions")
if (!dir.exists(save_dir)) dir.create(save_dir)
save_dir <- file.path(save_dir, format(Sys.time(), "%F_%H_%M_%S"))
if (!dir.exists(save_dir)) dir.create(save_dir)
fname <- file.path(save_dir, "fitness_function.html")
htmlwidgets::saveWidget(as_widget(p), fname)
```

Plot different trait architectures according to different algorithms
```{r}
save_dir <- file.path(output_dir, "TraitArchitecture")
if (!dir.exists(save_dir)) dir.create(save_dir)
save_dir <- file.path(save_dir, format(Sys.time(), "%F_%H_%M_%S"))
if (!dir.exists(save_dir)) dir.create(save_dir)
write.table(getParams(), file.path(save_dir, "params.txt"), col.names=FALSE, quote=FALSE, sep=":\t")

# Additive effects
methodType <- "Additive"
g <- plotTraitArchitecture(founderPop, methodType)
ggplot2::ggsave(filename = paste0("traitarchitecture_", methodType, ".pdf"),
                path=save_dir,
                device = "pdf")

# Effect of each allele on fitness
methodType <- "Fitness"
g <- plotTraitArchitecture(founderPop, methodType)
ggplot2::ggsave(filename = paste0("traitarchitecture_", methodType, ".pdf"),
                path=save_dir,
                device = "pdf")

p <- plot3dPopulationFitness(founderPop, calculateFitnessTwoTrait)

fname <- file.path(save_dir, "3dpopulationfitness.html")
htmlwidgets::saveWidget(as_widget(p), fname)
```

Simulate several adaptive walks with different population sizes
```{r}
# Different starting population sizes
subPopSizes <- c(50,500)
selProps <- c(0.4,0.4)
n.h2 <- 0.3
n.var <- 0.1
n.qtlPerChr <- 2
n.gens <- 50

# Don't use a SNP chip for these simulations
addSnpChip <- FALSE

fig <- plot_ly() %>%
  layout(legend=list(title=list(text='Population Size')),
         scene = list(xaxis = list(title = "Trait A"),
                      yaxis = list(title = "Trait B"),
                      zaxis = list(title = "Fitness"),
                      aspectmode='cube')) #hide_colorbar()

#source("Scripts/CreateFounderPop.R")
pop <- founderPop
for (gen in 1:n.burnInGens) {
  pop <- selectCross(pop, trait=twoTraitFitFunc, nInd=nInd(pop)*n.burnInSelProp, nCrosses=nInd(pop))
}

# Iterate through each population size
for (p in 1:length(subPopSizes)) {
  n.subPopSize <- subPopSizes[p]
  n.selProp <- selProps[p]
  print(n.subPopSize)
  fit_df <- data.frame(gen=1:n.gens,
                   fitness=numeric(n.gens),
                   traitValA=numeric(n.gens),
                   traitValB=numeric(n.gens))
  subPop <- selectInd(pop, nInd=n.subPopSize, use="rand")
  # Iterate through the generations, update the result dataframe, and advance
  # progeny based on the two trait fitness funcion
  for(gen in 1:n.gens) {
    meanFitness <- mean(twoTraitFitFunc(pheno(subPop)))
    fit_df$fitness[gen] <- calculateFitnessTwoTraitModified(meanP(subPop)[1], meanP(subPop)[2])
    fit_df$traitValA[gen] <- meanP(subPop)[1]
    fit_df$traitValB[gen] <- meanP(subPop)[2]
    selRat <- selectionRatio(meanFitness)
    subPop <- selectCross(subPop, trait=twoTraitFitFunc, nInd=n.subPopSize*selRat, nCrosses=n.subPopSize)
  }
  # Add a trace to represent this population's adaptive walk
  fig <- fig %>% add_trace(
    fig,
    fit_df,
    name = n.subPopSize,
    x = fit_df$traitValA,
    y = fit_df$traitValB,
    z = fit_df$fitness,
    type = 'scatter3d',
    mode = 'lines',
    opacity = 1,
    line = list(autocolorscale=FALSE, colors="PuBuGn", color=p, which=2, width = 10)
  )
}

# Create a matrix of fitness values, with a small increment along the x and y axes.
fitness_x = seq(n.initTraitVal*-1,n.initTraitVal*1, by=(n.initTraitVal/100))
fitness_y = seq(n.initTraitVal*-1,n.initTraitVal*1, by=(n.initTraitVal/100))
fitness_z = outer(fitness_x,fitness_y,calculateFitnessTwoTrait)

fig <- fig %>%
      add_trace(
        fig,
        x=fitness_x,
        y=fitness_y,
        z=fitness_z,
        type='surface',
        colorbar=list(title = "Fitness"),
        colors = viridis(n=10),
        opacity=1.0)


fig
save_dir <- file.path(output_dir, "DifferentPopulationSizes")
if (!dir.exists(save_dir)) dir.create(save_dir)
save_dir <- file.path(save_dir, format(Sys.time(), "%F_%H_%M_%S"))
if (!dir.exists(save_dir)) dir.create(save_dir)
write.table(getParams(), file.path(save_dir, "params.txt"), col.names=FALSE, quote=FALSE, sep=":\t")

fname <- file.path(save_dir, paste0("populationSizes_", subPopSizes[1], ",", subPopSizes[2], ",", subPopSizes[3], ".html"))
htmlwidgets::saveWidget(as_widget(fig), fname)
```

Overlay an adaptive walk over a fitness landscape, and save 3D and 2D versions
```{r}
fig <- plot_ly()
fit_df <- data.frame(gen=1:n.gens,
                   fitness=numeric(n.gens),
                   traitValA=numeric(n.gens),
                   traitValB=numeric(n.gens))

pop <- founderPop

for(gen in 1:n.gens) {
  fit_df$fitness[gen] <- mean(twoTraitFitFunc(gv(pop)))
  fit_df$traitValA[gen] <- meanG(pop)[1]
  fit_df$traitValB[gen] <- meanG(pop)[2]
  meanFitness <- mean(twoTraitFitFunc(pheno(pop)))
  selRat <- selectionRatio(meanFitness)
  pop <- selectCross(pop, trait=twoTraitFitFunc, nInd=n.popSize*selRat, nCrosses=n.popSize)
}

save_dir <- file.path(output_dir, "OverlayAdaptiveWalk")
if (!dir.exists(save_dir)) dir.create(save_dir)
save_dir <- file.path(save_dir, format(Sys.time(), "%F_%H_%M_%S"))
if (!dir.exists(save_dir)) dir.create(save_dir)

plotType <- "CONTOUR"
pc <- overlayWalkOnLandscape(fit_df, type=plotType, calculateFitnessTwoTrait)
fname <- file.path(save_dir, paste0("adaptivewalk_", plotType, ".html"))
htmlwidgets::saveWidget(as_widget(pc), fname)

plotType <- "SURFACE"
ps <- overlayWalkOnLandscape(fit_df, type=plotType, calculateFitnessTwoTrait)
fname <- file.path(save_dir, paste0("adaptivewalk_", plotType, ".html"))
htmlwidgets::saveWidget(as_widget(ps), fname)

write.table(getParams(), file.path(save_dir, "params.txt"), col.names=FALSE, quote=FALSE, sep=":\t")
```

Plot the change in allele frequency over time
```{r}
n.qtlPerChr <- 2
source("Scripts/CreateFounderPop.R")
pop <- founderPop

# Results dataframe
freq.df <- data.frame(gen=1:n.gens)

# Get the effect sizes of each qtl
qtlEff.df <- getQtlEffectSizes(pop)

# Get the names of all the QTLs
qtl <- colnames(getUniqueQtl(pop))

# Create a dataframe of all zeros where the columns are the QTL ids, and the # rows is the # of generations
qtl.df <- data.frame(matrix(0, ncol=length(qtl), nrow=n.gens))
colnames(qtl.df) <- qtl

# Combine the dataframes
freq.df <- cbind(freq.df, qtl.df)

# Iterate through each generation
for(gen in 1:n.gens) {
  meanFitness <- mean(twoTraitFitFunc(pheno(pop)))
  # Get the qtl genotype data
  qtlGeno <- getUniqueQtl(pop)
  # Get the frequency of the '2' allele at each locus
  for (l in 1:length(qtl)) {
    # id is the name of the qtl (chr_site)
    id <- qtl[l]
    # A list of genotype data for each individual in the population at that locus
    locus <- qtlGeno[,id]
    # Calculate the allele frequency as the frequency of homozygous individuals (for 'allele') +
    # 1/2 * frequency of heterozygous individuals (assumes the locus is biallelic)
    freq.df[gen,id] <- (sum(locus==n.allele)/n.popSize) + ((sum(locus==1)/n.popSize)/2)
  }
  # Determine selection ration based on fitness
  selRat <- selectionRatio(meanFitness)
  #Advance individuals
  pop <- selectCross(pop, trait=twoTraitFitFunc, nInd=n.popSize*selRat, nCrosses=n.popSize)
}

# Make the dataframe tidy
freq.df<- melt(freq.df, id="gen", variable.name="QTL ID", value.name="Allele Frequency")

# Add the qtl effect size data to the dataframe
freq.df <- merge(freq.df, qtlEff.df, by.x="QTL ID", by.y="row.names", all.x=TRUE)

# Create a line plot for the change in frequency of alleles over time
# Each line's opacity is a function of its effect size (higher=darker)
g <- ggplot(freq.df, aes(x=gen, y=freq, color=id, alpha=eff_size)) +
  geom_line(size=0.7)

save_dir <- file.path(output_dir, "AlleleFrequencies")
if (!dir.exists(save_dir)) dir.create(save_dir)
save_dir <- file.path(save_dir, format(Sys.time(), "%F_%H_%M_%S"))
if (!dir.exists(save_dir)) dir.create(save_dir)
write.table(getParams(), file.path(save_dir, "params.txt"), col.names=FALSE, quote=FALSE, sep=":\t")

ggplot2::ggsave(filename = paste0("allelefrequencies.pdf"),
                path=save_dir,
                device = "pdf",
                width=10,
                height=7)
```

Simulate an adaptive walk, and plot the following curve:
#segregating QTL/#segregating loci

Original question: out of segregating alleles left in the population, how many
of them would increase fitness? Is this even a valid question?

TODO:
- How does this change with size of allele?
- How does size of 'fitness delta' change over generations?
- Simualate mutations for a single individual to figure out P(allelic substitution is favorable),
should reflect FKO
- P(mutation gets fixed) - show that this matches Kimura
- Compare adaptive walks for DH/inbred population where there are new mutations VS
population w/standing genetic variation
```{r}
n.gens <- 200

# Reset variables
source("Scripts/CreateFounderPop.R")
source("Scripts/GlobalParameters.R")

pop <- founderPop
df <- data.frame(gen=c(),
                 fitness=c(),
                 traitVal1=c(),
                 traitVal2=c(),
                 percFitInc=c(),
                 segAlleles=c(),
                 segQtl=c())

# Iterate through the generations
for (gen in 1:n.gens) {
  print(gen)
  # Counter variables
  numHetLoci = 0
  numHetQtl = 0
  numInc = 0

  # Get all of the loci from the population
  segSiteGeno <- pullSegSiteGeno(pop)
  # Get all of the QTL from the population (a sampling of the segSiteGeno)
  qtlGeno <- getUniqueQtl(pop)
  qtlLoci <- colnames(qtlGeno)
  loci <- colnames(segSiteGeno)
  nLoci <- ncol(segSiteGeno)
  # Iterate through all of the loci
  # TODO: just calculate numHetQtl / numHetLoci
  for (l in 1:ncol(segSiteGeno)) {
    id <- loci[l]
    locus = segSiteGeno[,l]
    # Check if the locus is segregating
    if (hetLocus(locus)) {
      # Increment the counter
      numHetLoci <- numHetLoci + 1
      # Determine the effect size - this is where the bug is
      #e <- getEffectSize(locus, id, pop, "Fitness")
      #if (e > 0) {
      #  numInc <- numInc +1
      #}
      # If the locus is a QTL, then it has an effect size
      if (id %in% qtlLoci) {
        numHetQtl <- numHetQtl + 1
      }
    }
  }

  # Calculate the ratio of segregating QTL to segregating alleles
  if (numHetLoci == 0) {
    perc <- 0
  } else {
    perc <- (numHetQtl / numHetLoci)
  }
  df <- rbind(df, data.frame(gen=gen,
                             fitness=mean(twoTraitFitFunc(gv(pop))),
                             traitVal1=meanG(pop)[1],
                             traitVal2=meanG(pop)[2],
                             percFitInc=perc,
                             segAlleles=numHetLoci,
                             segQtl=numHetQtl))
  # If the population is within n.margin, terminate the simulation
  if (mean(twoTraitFitFunc(gv(pop))) >= n.margin) {
    break
  }
  meanFitness <- mean(twoTraitFitFunc(pheno(pop)))
  selRat <- selectionRatio(meanFitness)
  # Advance the top progeny according to the fitness function
  pop <- selectCross(pop=pop, trait=twoTraitFitFunc, nInd=nInd(pop)*selRat, nCrosses=nInd(pop))
}

save_dir <- file.path(output_dir, "BeneficialAlleles")
if (!dir.exists(save_dir)) dir.create(save_dir)
save_dir <- file.path(save_dir, format(Sys.time(), "%F_%H_%M_%S"))
if (!dir.exists(save_dir)) dir.create(save_dir)
write.table(getParams(), file.path(save_dir, "params.txt"), col.names=FALSE, quote=FALSE, sep=":\t")

fname <- file.path(save_dir, "beneficialalleles.pdf")
pdf(fname)

# Plot the ratio of segregating QTL to segregating alleles, along with fitness
par(mar = c(5, 5, 3, 5) + 0.3)
plot(df$gen, df$fitness, type="l", lwd = "3", col=2, xlab = "Generation", ylab = "Fitness")
par(new = TRUE) 
plot(df$gen, df$percFitInc, type="l", lwd = "3", col = 3, axes = FALSE, xlab = "", ylab = "") 
axis(side = 4, at = pretty(range(df$percFitInc)))
mtext("% of Segregating alleles that are QTL", side = 4, line = 3)
par(xpd=TRUE)
legend("right",
  c("Fitness", "% Alleles"),
       lty = 1,
       lwd = 3,
       col = 2:3)
dev.off()
```

This block runs n.nPops * n.sims Monte Carlo simulations of different populations to determine
the change in the average effect size of alleles that are fixed along the adaptive walk,
saving the order in which the alleles are fixed
n.nPops populations are created, and each one undergoes n.sims unique adaptive walks

```{r}
n.sims <- 200
n.popResets <- 5
n.gens <- 200
n.selProp <- 0.1
n.h2 <- 0.2
n.subPopSize <- 500
n.qtlPerChr <- 2
n.margin <- 0
addSnpChip <- FALSE

if (saveFitnessPlots) {
  fig <- plot_ly()
}

# Tidy dataframe to store the order in which each allele is fixed, and the effect size
effSize.df <- data.frame(orderFixed=c(),
                         gen=c(),
                         fitness=c(),
                          effectSize=c())
# Create a new population n.nPops times
for (p in 1:n.popResets) {
  print(paste0("Pop Reset: ", p))
  source("Scripts/CreateFounderPop.R")

  # Get the effect sizes of each qtl
  qtlEff.df <- getQtlEffectSizes(founderPop)
  
  # Get the names of all the QTLs
  qtl <- colnames(getUniqueQtl(founderPop))
  for (s in 1:n.sims) {
    pop <- founderPop
    if (saveFitnessPlots) {
      pop_df <- data.frame(gen=1:n.gens,
                         fitness=numeric(n.gens),
                         traitValA=numeric(n.gens),
                         traitValB=numeric(n.gens))
    }
    
  
    print(paste0("Sim: ", s))
    # idx is the order in which an allele is fixed along an adaptive walk
    idx <- 1
    # whether or not to increment the idx counter. Multiple alleles may be fixed
    # in the same generation, so this cannot be incremented until each locus
    # has been examined
    inc <- FALSE
    
    # Burn-in
    for (gen in 1:n.burnInGens) {
      pop <- selectCross(pop, trait=twoTraitFitFunc, nInd=nInd(pop)*n.burnInSelProp, nCrosses=nInd(pop))
    }

    # Select a subpopulation
    pop <- selectInd(pop, nInd=n.subPopSize, use="rand")
    
    # Main simulation
    for (gen in 1:n.gens) {
      meanFitness <- mean(twoTraitFitFunc(pheno(pop)))
      selRat <- selectionRatio(meanFitness)
      if (saveFitnessPlots) {
        pop_df$fitness[gen] <- meanFitness
        pop_df$traitValA[gen] <- meanP(pop)[1]
        pop_df$traitValB[gen] <- meanP(pop)[2]
      }
      # Keep track of the current population
      prevPop <- pop
      # Advance the population based on fitness
      pop <- selectCross(pop, trait=twoTraitFitFunc, nInd=nInd(pop)*selRat, nCrosses=nInd(pop))
      # Get the qtl genotypes from the current and new populations, so we can compare them
      # Determine whether each locus is segregating
      prevHet <- as.data.frame(apply(getUniqueQtl(prevPop), MARGIN=2, FUN=hetLocus))
      curHet <- as.data.frame(apply(getUniqueQtl(pop), MARGIN=2, FUN=hetLocus))
      # Join the two sets of genetic data
      loci <- cbind(prevHet, curHet)
      colnames(loci) <- c("prev", "cur")
      # Add a column called "fixed" which is true if the previous genotype was segregating
      # and the current genotype is not
      loci <- loci %>%
        mutate(fixed=mapply(function(p,c) (p && !c), prev, cur))
      
      # Iterate through all loci, and if the locus was fixed this generation,
      # add it to the result dataframe
      for (l in 1:length(qtl)) {
        id <- qtl[l]
        if (loci[id, "fixed"] == TRUE) {
          # Increment the order counter after this generation
          inc <- TRUE
          effSize.df <- rbind(effSize.df,
                              data.frame(orderFixed=c(idx),
                                         gen=c(gen),
                                         fitness=c(meanFitness),
                                         effectSize=c(qtlEff.df[id,1])))
        }
      }
      # Check whether to increment the order counter and reset 'inc'
      if (inc) {
        idx <- idx + 1
        inc <- FALSE
      }
      # If all alleles are fixed, terminate the simulation
      if(!any(curHet)) {
        break
      }
      # If the population is within n.margin, terminate the simulation
      if (mean(twoTraitFitFunc(pheno(pop))) >= n.margin) {
        break
      }
      
    }
    # Add the adaptive walk of the sub-population
    if (saveFitnessPlots) {
      fig <- add_trace(
        fig,
        pop_df,
        name = s,
        x = pop_df$traitValA,
        y = pop_df$traitValB,
        z = pop_df$fitness,
        type = 'scatter3d',
        mode = 'lines',
        opacity = 1,
        color = s,
        line = list(width = 2)
      )
    }
  }
}

save_dir <- file.path(output_dir, "AverageEffectSize")
if (!dir.exists(save_dir)) dir.create(save_dir)
save_dir <- file.path(save_dir, format(Sys.time(), "%F_%H_%M_%S"))
if (!dir.exists(save_dir)) dir.create(save_dir)
write.table(getParams(), file.path(save_dir, "params.txt"), col.names=FALSE, quote=FALSE, sep=":\t")
write.table(effSize.df, file.path(save_dir, "effSize.csv"), col.names=TRUE, quote=FALSE, sep=",")

# Determine the average additive effect size at each 'step'
groupedEffSize.df <- effSize.df %>%
  mutate(smoothedFit=round(fitness,digits=1)/-4) %>%
  group_by(smoothedFit) %>%
  filter(!(abs(effectSize - median(effectSize)) > 2*sd(effectSize))) %>%
  summarize(meanEffectSize = mean(effectSize), n=n()) %>%
  filter(n > 100)

g <- ggplot(groupedEffSize.df, aes(x=smoothedFit, y=meanEffectSize)) +
  geom_point() +
  scale_x_reverse() +
  theme +
  labs(title="Figure 1", x="Normalized Distance from Fitness Optimum", y="Mean Effect Size") +
  geom_smooth(formula = y ~ x)

ggplot2::ggsave(filename = "average_effect_size_additive.pdf",
                path=save_dir,
                device = "pdf")

# Create a plot with the adaptive walks
if (saveFitnessPlots) {
  p <- fig %>%
    layout(legend=list(title=list(text='Population')),
           showlegend=FALSE,
           scene = list(xaxis = list(title = "Trait A"),
                        yaxis = list(title = "Trait B"),
                        zaxis = list(title = "Fitness"),
                        aspectmode='cube')) %>% hide_colorbar()
  fname <- file.path(save_dir, "adaptivewalk.html")
  htmlwidgets::saveWidget(as_widget(p), fname)
}
```

This block will simulate one base population, from which two sub-populations are selected, and undergo purifying selection independently.
```{r}
source("Scripts/GlobalParameters.R")
source("Scripts/CreateFounderPop.R")

fit_df <- data.frame(gen=1:n.burnInGens,
                 fitness=numeric(n.burnInGens),
                 traitValA=numeric(n.burnInGens),
                 traitValB=numeric(n.burnInGens))
pop <- founderPop

# Burn-in generations
for (gen in 1:n.burnInGens) {
  fit_df$fitness[gen] <- mean(twoTraitFitFunc(pheno(pop)))
  fit_df$traitValA[gen] <- meanP(pop)[1]
  fit_df$traitValB[gen] <- meanP(pop)[2]
  pop <- selectCross(pop, trait=twoTraitFitFunc, nInd=nInd(pop)*n.burnInSelProp, nCrosses=nInd(pop))
}

# Create a random vector of size n.pops, with a random order of sub-population ids
randVec <- sample(rep(c(1:n.nPops), times=n.popSize/n.nPops))

# Select all of the "1" indexed individuals
popA <- selectInd(pop, trait=selectSubPop, selectTop=TRUE, nInd=n.subPopSize, idx=1, randVec=randVec)
# Select all of the "2" indexed individuals
popB <- selectInd(pop, trait=selectSubPop, selectTop=TRUE, nInd=n.subPopSize, idx=2, randVec=randVec)

# Create dataframes for each subpopulation, initializing with current values
popA_df <- data.frame(gen=c(1),
                 fitness=c(mean(twoTraitFitFunc(pheno(popA)))),
                 traitValA=c(meanP(popA)[1]),
                 traitValB=c(meanP(popA)[2]))
popB_df <- data.frame(gen=c(1),
                 fitness=c(mean(twoTraitFitFunc(pheno(popB)))),
                 traitValA=c(meanP(popB)[1]),
                 traitValB=c(meanP(popB)[2]))

# Iterate through the generations
for (gen in 1:n.gens) {
  # If popA is within the margin of the fitness optimum, don't progress it any further
  if (mean(twoTraitFitFunc(pheno(popA))) < n.margin) {
    # Advance the population
    meanFitness <- mean(twoTraitFitFunc(pheno(popA)))
    # Get a selection ratio based on fitness
    selRat <- selectionRatio(meanFitness)
    popA <- selectCross(popA, trait=twoTraitFitFunc, nInd=nInd(popA)*selRat, nCrosses=nInd(popA))
    # Update the dataframe with new values
    popA_df <- rbind(popA_df, data.frame(gen=gen,
                                       fitness=meanFitness,
                                       traitValA=meanP(popA)[1],
                                       traitValB=meanP(popA)[2]))
    
  }
  # If popB is within the margin of the fitness optimum, don't progress it any further
  if (mean(twoTraitFitFunc(pheno(popB))) < n.margin) {
    # If popA is within the margin of the fitness optimum, don't progress it any further
    meanFitness <- mean(twoTraitFitFunc(pheno(popB)))
    # Get a selection ratio based on fitnes
    selRat <- selectionRatio(meanFitness)
    popB <- selectCross(popB, trait=twoTraitFitFunc, nInd=nInd(popB)*selRat, nCrosses=nInd(popB))
    # Update the dataframe with new values
    popB_df <- rbind(popB_df, data.frame(gen=gen,
                                       fitness=meanFitness,
                                       traitValA=meanP(popB)[1],
                                       traitValB=meanP(popB)[2]))
  }
}
# Update rownames
rownames(popA_df) <- 1:nrow(popA_df)
rownames(popB_df) <- 1:nrow(popB_df)

# Plot the adaptive walks
fig <- plot_ly()
fig <- add_trace(
  fig,
  fit_df,
  name = "Burn In",
  x = fit_df$traitValA,
  y = fit_df$traitValB,
  z = fit_df$fitness,
  type = 'scatter3d',
  mode = 'lines',
  opacity = 1,
  color = 'yellow',
  line = list(width = 5)
)

fig <- add_trace(
    fig,
    popA_df,
    name = "Pop A",
    x = popA_df$traitValA,
    y = popA_df$traitValB,
    z = popA_df$fitness,
    type = 'scatter3d',
    mode = 'lines',
    opacity = 1,
    color = 'red',
    line = list(width = 5)
  )

fig <- add_trace(
    fig,
    popB_df,
    name = "Pop B",
    x = popB_df$traitValA,
    y = popB_df$traitValB,
    z = popB_df$fitness,
    type = 'scatter3d',
    mode = 'lines',
    opacity = 1,
    color = 'blue',
    line = list(width = 5)
  )


p <- fig %>%
  layout(legend=list(title=list(text='Population')),
         showlegend=FALSE,
         scene = list(xaxis = list(title = "Trait A"),
                      yaxis = list(title = "Trait B"),
                      zaxis = list(title = "Fitness"),
                      aspectmode='cube')) %>% hide_colorbar()

save_dir <- file.path(output_dir, "DivergingPopulations")
if (!dir.exists(save_dir)) dir.create(save_dir)
save_dir <- file.path(save_dir, format(Sys.time(), "%F_%H_%M_%S"))
if (!dir.exists(save_dir)) dir.create(save_dir)
fname <- file.path(save_dir, "adaptivewalks.html")
htmlwidgets::saveWidget(as_widget(p), fname)

fname <- file.path(save_dir, "2PopulationFitness.html")
p <- plot3dPopulationFitnessTwoPops(popA, popB)
htmlwidgets::saveWidget(as_widget(p), fname)
write.table(getParams(), file.path(save_dir, "params.txt"), col.names=FALSE, quote=FALSE, sep=":\t")

fname <- file.path(save_dir, "traitarchitecture.pdf")
pdf(fname)

p1 <- plotTraitArchitecture(popA, "Fitness", "popA")
p2 <- plotTraitArchitecture(popB, "Fitness", "popB")
p3 <- plotTraitArchitecture(popA, "Additive", "popA")
p4 <- plotTraitArchitecture(popB, "Additive", "popB")

(p1|p2)/(p3|p4)
dev.off()

# Create a density plot of trait 1
trait1.df <- as.data.frame(cbind(pheno(popA)[,1], pheno(popB)[,1]))
colnames(trait1.df) <- c("popA", "popB")
trait1.df <- trait1.df %>%
  pivot_longer(c("popA", "popB"), names_to="pop", values_to="pheno")
t1 <- ggplot(trait1.df, aes(pheno, fill=pop, color=pop)) +
  geom_density(alpha=0.1) +
  labs(title="Trait 1")

# Create a density plot of trait 2
trait2.df <- as.data.frame(cbind(pheno(popA)[,2], pheno(popB)[,2]))
colnames(trait2.df) <- c("popA", "popB")
trait2.df <- trait2.df %>%
  pivot_longer(c("popA", "popB"), names_to="pop", values_to="pheno")
t2 <- ggplot(trait2.df, aes(pheno, fill=pop, color=pop)) +
  geom_density(alpha=0.1) +
  labs(title="Trait 2")


(t1|t2)
ggplot2::ggsave(filename = "trait_distributions.pdf",
                path=save_dir,
                device = "pdf")


```

```{r}
source("Scripts/GlobalParameters.R")
n.qtlPerChr <- 2
n.h2 <- 0.3
n.qtlPerChr <- 2
n.gens <- 100
#source("Scripts/CreateFounderPop.R")
n.selProp <- 0.5
n.subPopSize <- 50

fit_df <- data.frame(gen=1:n.burnInGens,
                 fitness=numeric(n.burnInGens),
                 traitValA=numeric(n.burnInGens),
                 traitValB=numeric(n.burnInGens))
pop <- founderPop

# Burn-in generations
for (gen in 1:n.burnInGens) {
  fit_df$fitness[gen] <- mean(twoTraitFitFunc(pheno(pop)))
  fit_df$traitValA[gen] <- meanP(pop)[1]
  fit_df$traitValB[gen] <- meanP(pop)[2]
  pop <- selectCross(pop, trait=twoTraitFitFunc, nInd=nInd(pop)*n.burnInSelProp, nCrosses=nInd(pop))
}

# Create a random vector of size n.pops, with a random order of sub-population ids
randVec <- sample(rep(c(1:n.nPops), times=n.popSize/n.nPops))

# Select all of the "1" indexed individuals
popA <- selectInd(pop, trait=selectSubPop, selectTop=TRUE, nInd=n.subPopSize, idx=1, randVec=randVec)
# Select all of the "2" indexed individuals
popB <- selectInd(pop, trait=selectSubPop, selectTop=TRUE, nInd=n.subPopSize, idx=2, randVec=randVec)

# Create dataframes for each subpopulation, initializing with current values
popA_df <- data.frame(gen=c(1),
                 fitness=c(mean(twoTraitFitFunc(pheno(popA)))),
                 traitValA=c(meanP(popA)[1]),
                 traitValB=c(meanP(popA)[2]))
popB_df <- data.frame(gen=c(1),
                 fitness=c(mean(twoTraitFitFunc(pheno(popB)))),
                 traitValA=c(meanP(popB)[1]),
                 traitValB=c(meanP(popB)[2]))

# Iterate through the generations
for (gen in 1:n.gens) {
  # If popA is within the margin of the fitness optimum, don't progress it any further
  if (mean(twoTraitFitFunc(pheno(popA))) < n.margin) {
    # Advance the population
    meanFitness <- mean(twoTraitFitFunc(pheno(popA)))
    # Get a selection ratio based on fitness
    selRat <- selectionRatio(meanFitness)
    popA <- selectCross(popA, trait=twoTraitFitFunc, nInd=nInd(popA)*selRat, nCrosses=nInd(popA))
    # Update the dataframe with new values
    popA_df <- rbind(popA_df, data.frame(gen=gen,
                                       fitness=meanFitness,
                                       traitValA=meanP(popA)[1],
                                       traitValB=meanP(popA)[2]))
    
  }
  # If popB is within the margin of the fitness optimum, don't progress it any further
  if (mean(twoTraitFitFunc(pheno(popB))) < n.margin) {
    # If popA is within the margin of the fitness optimum, don't progress it any further
    meanFitness <- mean(twoTraitFitFunc(pheno(popB)))
    # Get a selection ratio based on fitnes
    selRat <- selectionRatio(meanFitness)
    popB <- selectCross(popB, trait=twoTraitFitFunc, nInd=nInd(popB)*selRat, nCrosses=nInd(popB))
    # Update the dataframe with new values
    popB_df <- rbind(popB_df, data.frame(gen=gen,
                                       fitness=meanFitness,
                                       traitValA=meanP(popB)[1],
                                       traitValB=meanP(popB)[2]))
  }
}
# Update rownames
rownames(popA_df) <- 1:nrow(popA_df)
rownames(popB_df) <- 1:nrow(popB_df)

# Plot the adaptive walks
# Create a matrix of fitness values, with a small increment along the x and y axes.
fitness_x = seq(n.initTraitVal*-1,n.initTraitVal*1, by=(n.initTraitVal/100))
fitness_y = seq(n.initTraitVal*-1,n.initTraitVal*1, by=(n.initTraitVal/100))
fitness_z = outer(fitness_x,fitness_y,calculateFitnessTwoTrait)

fig <- plot_ly() %>%
      layout(xaxis = list(title = "Trait A", constrain = "domain"),
             yaxis = list(title = "Trait B", scaleanchor="x")) %>%
      add_trace(
        fig,
        x=fitness_x,
        y=fitness_y,
        z=fitness_z,
        type='contour',
        colors = viridis(n=10),
        colorbar=list(title = "Fitness"),
        line = list(color = 'black', width = 1),
        opacity=1) %>%
      add_trace(
        fig,
        fit_df,
        name = "Burn-in",
        x = fit_df$traitValA,
        y = fit_df$traitValB,
        type='scatter',
        mode = 'lines',
        line = list(color = 'orange', width = 4, dash = 'solid'),
        opacity = 1) %>%
      add_trace(
        fig,
        popA_df,
        name = "Subpop 1",
        x = popA_df$traitValA,
        y = popA_df$traitValB,
        type='scatter',
        mode = 'lines',
        line = list(color = 'red', width = 4, dash = 'solid'),
        opacity = 1) %>%
      add_trace(
        fig,
        popB_df,
        name = "Subpop 2",
        x = popB_df$traitValA,
        y = popB_df$traitValB,
        type='scatter',
        mode = 'lines',
        line = list(color = 'blue', width = 4, dash = 'solid'),
        opacity = 1)

save_dir <- file.path(output_dir, "DivergingPopulations")
if (!dir.exists(save_dir)) dir.create(save_dir)
save_dir <- file.path(save_dir, format(Sys.time(), "%F_%H_%M_%S"))
if (!dir.exists(save_dir)) dir.create(save_dir)
fname <- file.path(save_dir, "adaptivewalks.html")
htmlwidgets::saveWidget(as_widget(fig), fname)

fname <- file.path(save_dir, "2PopulationFitness.html")
p <- plot3dPopulationFitnessTwoPops(popA, popB)
htmlwidgets::saveWidget(as_widget(p), fname)
write.table(getParams(), file.path(save_dir, "params.txt"), col.names=FALSE, quote=FALSE, sep=":\t")

fname <- file.path(save_dir, "traitarchitecture.pdf")
pdf(fname)

p1 <- plotTraitArchitecture(popA, "Fitness", "popA")
p2 <- plotTraitArchitecture(popB, "Fitness", "popB")
p3 <- plotTraitArchitecture(popA, "Additive", "popA")
p4 <- plotTraitArchitecture(popB, "Additive", "popB")

(p1|p2)/(p3|p4)
dev.off()

# Create a density plot of trait 1
trait1.df <- as.data.frame(cbind(pheno(popA)[,1], pheno(popB)[,1]))
colnames(trait1.df) <- c("popA", "popB")
trait1.df <- trait1.df %>%
  pivot_longer(c("popA", "popB"), names_to="pop", values_to="pheno")
t1 <- ggplot(trait1.df, aes(pheno, fill=pop, color=pop)) +
  geom_density(alpha=0.1) +
  labs(title="Trait 1")

# Create a density plot of trait 2
trait2.df <- as.data.frame(cbind(pheno(popA)[,2], pheno(popB)[,2]))
colnames(trait2.df) <- c("popA", "popB")
trait2.df <- trait2.df %>%
  pivot_longer(c("popA", "popB"), names_to="pop", values_to="pheno")
t2 <- ggplot(trait2.df, aes(pheno, fill=pop, color=pop)) +
  geom_density(alpha=0.1) +
  labs(title="Trait 2")


(t1|t2)
ggplot2::ggsave(filename = "trait_distributions.pdf",
                path=save_dir,
                device = "pdf")
```

